JANOS: An Integrated Predictive and Prescriptive Modeling Framework

    •  Bergman, David, Huang, Teng, Brooks, Philip, Lodi, Andrea, Raghunathan, Arvind, "JANOS: An Integrated Predictive and Prescriptive Modeling Framework", Tech. Rep. TR2021-025, Mitsubishi Electric Research Laboratories, Cambridge, MA, April 2021.
      BibTeX TR2021-025 PDF
      • @techreport{MERL_TR2021-025,
      • author = {Bergman, David; Huang, Teng; Brooks, Philip; Lodi, Andrea; Raghunathan, Arvind},
      • title = {JANOS: An Integrated Predictive and Prescriptive Modeling Framework},
      • institution = {MERL - Mitsubishi Electric Research Laboratories},
      • address = {Cambridge, MA 02139},
      • number = {TR2021-025},
      • month = apr,
      • year = 2021,
      • url = {}
      • }
  • MERL Contact:
  • Research Areas:

    Machine Learning, Optimization


Business research practice is witnessing a surge in the integration of predictive modeling and prescriptive analysis. We describe a modeling framework JANOS that seamlessly integrates the two streams of analytics, allowing researchers and practitioners to embed machine learning models in an end-to-end optimization framework. JANOS allows for specifying a prescriptive model using standard optimization modeling elements such as constraints and variables. The key novelty lies in providing modeling constructs that enable the specification of commonly used predictive models within an optimization model, have the features of the predictive model as variables in the optimization model, and incorporate the output of the predictive models as part of the objective. The framework considers two sets of decision variables; regular and predicted. The relationship between the regular and the predicted variables are specified by the user as pre-trained predictive models. JANOS currently supports linear regression, logistic regression, and neural network with rectified linear activation functions. In this paper, we demonstrate the flexibility of the framework through an example on scholarship allocation in a student enrollment problem and provide a numeric performance evaluation.


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    •  NEWS    Arvind Raghunathan's publication is Featured Article in the current issue of the INFORMS Journal on Computing
      Date: April 1, 2022
      Where: INFORMS Journal on Computing (
      MERL Contact: Arvind Raghunathan
      Research Areas: Artificial Intelligence, Machine Learning, Optimization
      • Arvind Raghunathan co-authored a publication titled "JANOS: An Integrated Predictive and Prescriptive Modeling Framework" which has been chosen as a Featured Article in the current issue of the INFORMS Journal on Computing. The article was co-authored with Prof. David Bergman, a collaborator of MERL and Teng Huang, a former MERL intern, among others.

        The paper describes a new software tool, JANOS, that integrates predictive modeling and discrete optimization to assist decision making. Specifically, the proposed solver takes as input user-specified pretrained predictive models and formulates optimization models directly over those predictive models by embedding them within an optimization model through linear transformations.